Effectiveness in reflecting uncertainties in a modeling process is critical for ensuring forecasting accuracy and improving process efficiency. However, few previous studies made efforts toward incorporating uncertainties into composting modeling systems. The purpose of this study is to develop a methodology based on Monte Carlo simulation and factorial design to reflect uncertainties in the modeling process. The uncertain parameters under consideration include temperature, moisture content, free air space, and oxygen concentration. Monte Carlo simulations are undertaken based on composting kinetic simulator, and a set of 24 factorial experiments are conducted to examine the effects of the input uncertainties and their interactions. The results indicate that complexities of the system uncertainties have been effectively addressed through the developed modeling approach. The proposed method offers an effective tool for composting process simulation and control under uncertainty.